Building your path to escape from home

TL;DR

This paper introduces the Bernoulli Growth Random Walk model on trees, demonstrating the walker's transience, positive linear speed, and convergence of the observed tree to a one-ended structure, advancing understanding of dynamic graph exploration.

Contribution

It presents the BGRW model, analyzing its transience, linear speed, and the limiting structure of the observed tree, which was previously unexplored in this context.

Findings

Walker is transient with positive linear speed for all p>0

The observed tree converges to a one-ended random tree

The model provides new insights into dynamic graph exploration

Abstract

Random walks on dynamic graphs have received increasingly more attention from different academic communities over the last decade. Despite the relatively large literature, little is known about random walks that construct the graph where they walk while moving around. In this paper we study one of the simplest conceivable discrete time models of this kind, which works as follows: before every walker step, with probability $p$ a new leaf is added to the vertex currently occupied by the walker. The model grows trees and we call it the Bernoulli Growth Random Walk (BGRW). We show that the BGRW walker is transient and has a well-defined linear speed $c(p)>0$ for any $0<p\leq 1$. We also show that the tree as seen by the walker converges (in a suitable sense) to a random tree that is one-ended. Some natural open problems about this tree and variants of our model are collected at the end of the paper.